This paper proposes a deep joint source-channel coding (DJSCC) to minimize the age of information (AoI) for image transmission. A new content-based AoI metric called age of misclassified information (AoMI) is introduced to estimate the freshness of the information in an image classification system. AoMI is a critical metric in timely information delivery, measuring the age of the most recently received and correctly classified image at the receiver. The proposed system leverages a deep neural network at the transmitter to map image pixels directly to channel input symbols, eliminating the need for separate source and channel coding. At the receiver, the channel output is processed to perform image classification. To analyze the AoMI performance of the system, a stochastic hybrid systems (SHS) approach is employed. Closed-form expressions for the average AoMI (AAoMI) are derived, providing insights into the impact of system parameters on the AoMI. Simulation results demonstrate the effectiveness of the proposed DJSCC-based system in achieving lower AoMI compared to traditional separate source and channel coding schemes. The findings highlight the potential of deep learning techniques to maintain the freshness of the information in wireless communication systems. This work paves the way for the design of wireless communication systems that prioritize the freshness of delivered information-this is crucial in applications such as real-time monitoring, surveillance, and control systems.

With the increasing occurrence of wildfires globally, quick and effective detection methods are vital. This paper proposes an innovative solution for wildfire detection using Unmanned Aerial Vehicle (UAV)-assisted detection systems. On the other hand, semantic communication, a technology designed for efficient data transmission in specialized tasks, plays a crucial role in next-generation wireless communications systems. In this paper, the deep joint source-channel coding (DJSCC) scheme has been used for efficient image transmission as a deep learning-based semantic communication technique for wildfire detection. DJSCC improves source and channel coding for semantic communications, offering advantages such as improved energy efficiency, reduced latency, and improved reliability compared to traditional source and channel code schemes. In this paper, the transmitter-receiver operations of the UAV communication system are modeled as a DJSCC, and they are jointly trained while taking into account the effects of the fading channel. The encoder transforms captured images into compact feature vectors, subsequently transmitting them using a reduced number of channels to minimize latency. Rather than engaging in the reconstruction of the input image in the receiver, the classifier performs a classification task using the received signals at the receiver. Alternatively, if the recovery of an image is required to understand the spread of the wildfire, the decoder reconstructs it by using the received signal at the receiver.

Simultaneous wireless information and power transfer (SWIPT) enabled wireless cooperative communication system is an emerging technology for future wireless communication applications. Furthermore, the Internet of Things (IoT) serves a diverse range of purposes, some of which are mission-critical and require constantly evolving real-time data. Thus, the information received at the destination must be updated timely manner to ensure its freshness. A performance metric named age of information (AoI) has been introduced to measure the freshness of received information. This study estimates the AoI of a SWIPT assisted decode and forward two-way relay assisted status update system in which two sources attempt to exchange status updates as quickly as possible to the destination. The relay system employs short packet communication to adhere to the latency and reliability requirements of the wireless communication system. We study the average Age of Information (AAoI) at the destination in the proposed relay network and derive approximations for the weighted sum AAoI under two different types of transmission scheduling policies at the relay: transmit without waiting (TWW) and wait until charged (WUC). Furthermore, the effects of transmission power, packet size, the distance between relay and sources and block-length on the weighted sum AAoI of the proposed SWIPT assisted short packet relay network are extensively investigated. The performance differences of the considered transmission policies are compared and insights are provided. Numerical simulations using the Monte Carlo method have been employed to validate derived analytical expressions.